Protopia AI introduces Roundtrip Protection, the only solution that eliminates plaintext exposure across the entire AI inference lifecycle enabling clients to retain ownership of their prompts and responses at all times.
This solution ensures only clients see complete data in plain-text, even in multi-tenant managed inference endpoint environments.
Our partnership with Lambda brings enterprises the ability to maintain full ownership of their sensitive data when using it with AI while taking advantage of Lambda’s market-leading inference offerings that optimize for price and performance.
For organizations deploying AI in regulated industries, or seeking to scale infrastructure with cost efficiency and state-of-the-art security, Protopia Roundtrip Protection + Lambda closes the final data privacy gap without compromising your ROI, sacrificing performance, or latency.
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Protopia Roundtrip Protection: The Only Solution to Eliminate Plaintext Exposure
At Protopia, we believe that enterprise clients should be the only ones who see the full data picture. But in today’s modern AI pipelines when LLMs are involved, that’s never the case.
Protopia’s Roundtrip Protection is the first solution to fully eliminate plaintext exposure across the AI inference lifecycle, ensuring no sensitive data is exposed outside the client’s zone of trust, from prompt input to outputs.
This establishes a zero-trust approach to data privacy that ensures plaintext data is never exposed in its entirety, neither to the large language model nor to the infrastructure it is hosted on. As a result, no part of the GenAI interaction is ever visible to those with unauthorized access.
The Hidden Attack Surface: Why Inference Is GenAI’s Most Vulnerable Step
LLMs fundamentally cannot run on encrypted data. This reality creates an inherent security gap during inference that grows with LLM memory capabilities and agentic workflows. Even with industry-standard encryption protocols that secure data in transit and at rest, sensitive data becomes exposed on hosting compute infrastructure in plaintext the moment it reaches an inference endpoint.
This exposure creates significant risk: a single misconfigured container or bad user password on the target compute system can leave enterprise private data exposed to unauthorized users sometimes without even triggering security alerts.
Rethinking What it Takes to Retain Ownership of Your Data with Managed Inference
As AI adoption accelerates, the challenge in achieving ROI from AI infrastructure often becomes a bottleneck for enterprise deployment. To reduce total cost of ownership and accelerate AI use-case adoption, more teams are turning to managed inference platforms like Lambda Cloud’s solution for offering Inference Endpoints. While this unlocks scalability, it also introduces a new data control gap: sensitive enterprise data becomes visible on infrastructure the organization doesn’t fully control.
To mitigate inference-layer data exposure, teams today are forced through a series of painful steps that ultimately don’t allow them to fully take advantage of their proprietary data with AI with acceptable ROI on their infra:
- Block access with firewalls – This method blocks LLM requests entirely if they include proprietary or sensitive information, eliminating GenAI’s inherent business value for the most important enterprise use-cases needed for creating efficiencies or revenue.
- Data Masking or Entity Redaction – These methods degrade model performance, and do not secure unstructured data holistically, or when precise outputs are needed for orchestration pipelines or function calling workflows.
- Deploy models on-prem to retain control over the compute environment – While this method avoids exposure, it introduces high infrastructure costs and slows time to market, which severely impacts ROI and Gen AI adoption.
With Roundtrip Protection + Lambda, enterprises no longer have to choose between price, performance, and privacy. Enterprises can now achieve all three and accelerate their time to value with LLMs.
Figure 1: Roundtrip Inference Data Protection: Illustrative example of how Protopia SGT™ and Lambda Cloud provide secure inference endpoints
How Protopia + Lambda Secures the AI Inference Lifecycle
Protopia Roundtrip Protection + Lambda closes the final privacy gap in the GenAI inference lifecycle by eliminating plaintext exposure without sacrificing performance.
Protopia’s Stained Glass Transform (SGT) creates a randomized representation of data before it leaves the client, preserving model accuracy while protecting the original input. Our proprietary stochastic transformation algorithm ensures sensitive information never appears in plaintext on the infrastructure hosting your target LLM.
Transformed data is processed by models on Lambda‘s infrastructure with the output only readable on the end-user client using the client’s private output key.
Whether you’re scaling internal GenAI tools or building enterprise-grade AI products, Roundtrip Protection ensures your data stays yours.
“Enterprise AI can’t succeed in production without unlocking the most relevant internal data to flow into the most cost-efficient and scalable inference, enabling models to generate the most trusted, accurate responses. Yet many projects stall at this point, caught between the promise of managed LLM endpoints and the risk of exposing sensitive information. Our partnership with Lambda Labs marks a new chapter where privacy, performance, and scalability go hand-in-hand, and where Lambda’s top-tier price/performance inference infrastructure is accessible with data privacy preserved across the entire roundtrip, from client to cloud to client.”
– Eiman Ebrahimi, Co-Founder and CEO, Protopia AI
“We’re excited to integrate Lambda’s high-performance LLM inference platform with Protopia’s roundtrip data protection to enable enterprises in regulated industries to operationalize advanced AI models securely. This combined solution allows organizations to scale inference workloads efficiently, preserve data confidentiality end-to-end, and accelerate deployment of state-of-the-art models with confidence.”
– Maxx Garrison, Director of Product Management, Lambda
Real-World Example: Patient notes summarization at Large Healthcare provider
Imagine a clinician at a Large Healthcare provider is using an AI Assistant to generate a concise summary of a patient’s telehealth consultation. The assistant draws on data such as symptom descriptions, prior diagnoses, medications, and clinician observations to create accurate, usable summaries for follow-up care and documentation for standard of care verification. This AI Assistant uses Lambda inference endpoint secured with Protopia’s SGT.
Faced with strict privacy concerns and the sensitivity of patient data, healthcare providers have traditionally avoided hosted LLMs or have had to accept significant risks to protected health information. By securing the workflow with Protopia’s SGT, the clinician’s patient notes are transformed before being sent to Lambda’s hosted LLM, which processes the protected data without ever the need for plain-text data. The AI-generated summary is then sent back to the authorized clinical staff user where the response is decrypted back to plain-text for the client.
Having the full ownership of their input prompt and the response enables the Healthcare Provider to adopt Gen AI in a cost effective and secure manner for increased productivity and efficiency in operations.
Get Started with Protopia + Lambda
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